Infrastructure for Parallel Processing Research

该奖项提供支持，以开发计算基础设施， 并行语言和编译器领域，自动并行化 算法，计算机视觉，模拟和合成 数字设备。 基础设施包括最先进的 单指令多数据（SIMD）计算机沿着支持 确保计算机硬件和软件正确运行。 树立政治意识 并行计算机的并行语言和编译程序研究 集中在问题的依赖，有效的软件上的 底层并行架构。 这种依赖性使得 开发便携式软件，这也是有效的困难， 到目前为止不可能。 研究的重点是语言， 并行性可以不受体系结构假设的约束地表达， 编译程序的编译器使用这些体系结构 将自由构造转换为高效代码， 建筑 算法的自动并行化研究主要集中在 有效地将算法映射到SIMD计算机的问题。 所述SIMD 计算机通过让许多处理器执行相同的操作来执行 不同数据上的指令。 因此，数据的移动和 选择哪一个处理器应该执行指令是至关重要的 以获得高性能。 这项研究的重点是一个 一种可适用于各种SIMD架构的软件工具 并且其将执行要使用的算法的并行化 with these architectures架构. 计算机视觉和图像处理一直是 因为SIMD计算机对图像的处理可以被分解 子图像的处理，每个子图像算法是 同样，随后是处理器之间的统一数据移动。 的 计算机视觉研究旨在开发一系列算法， 用于视觉和图像处理，将有效地使用SIMD 架构 数字信号模拟与综合的研究课题 设备使用SIMD计算机的处理能力来执行任务 这需要超级计算机的计算能力。 这些 项目包括数字衍射元件的合成和 模拟半导体器件的操作。 合成 数字衍射元件，用于光学，需要大量的 计算能力和输入/输出能力（大约 在VAX 780上运行10，000小时）。 同样，半导体的建模 设备需要大量的计算资源（ 30，000阶稀疏矩阵）。 这两个研究项目 建议开发用于SIMD计算机的算法，以便 获得解决以下问题所需的计算能力 这种规模。//

This award provides support to develop computing infrastructure in the areas of parallel languages and compilers, automatic parallelization of algorithms, computer vision, and simulation and synthesis of digital devices. The infrastructure consists of a state of the art Single Instruction Multiple Data (SIMD) computer along with support staff to ensure that the computer hardware and software are properly maintained. The parallel languages and compilers for parallel computers research focusses on the problem of the dependence of efficient software on the underlying parallel architecture. This dependence makes the development of portable software which is also efficient difficult and to date impossible. The research focusses on languages in which parallelism can be expressed free of architecture assumptions and compilers that will compile programs using these architecture free constructs into efficient code for specific parallel architectures. The automatic parallelization of algorithms research focusses on the problem of efficiently mapping algorithms to SIMD computers. The SIMD computer executes by having many processors execute the same instruction on different data. Thus the movement of data and the choice of which processors should execute an instruction is critical in order to obtain high performance. This research focusses on a software tool that can be adapted to a variety of SIMD architectures and which will perform the parallelization of algorithms to be used with these architectures. Computer vision and image processing have been ideal applications for SIMD computers because the processing of images can be broken down into the processing of sub-images, each sub-image algorithm being the same, followed by uniform data movement between processors. The computer vision research aims to develop a collection of algorithms for vision and image processing that will efficiently use the SIMD architecture. The research projects on the simulation and synthesis of digital devices use the processing power of the SIMD computer to perform tasks that would require supercomputer computational capabilities. These projects include the synthesis of digital diffractive elements and the simulation of the operation of semiconductor devices. The synthesis of digital diffractive elements, used in optics, requires substantial computational capabilities and input/output capacity (on the order of 10,000 hours on a VAX 780). Similarly the modeling of semiconductor devices requires substantial computational resources(the solution of order 30,000 sparse matrices). Both of these research projects propose to develop algorithms for use on the SIMD computer in order to obtain the computational capability necessary to solve problems of this magnitude. //